How to Use Python to Compare a text List with a dictionary of Words

Learn how to effectively count occurrences of words in a list compared to a dictionary in Python, avoiding common pitfalls and using helpful libraries.
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Counting Word Dictionary in Python: A Step-by-Step Guide

In programming, you often encounter the need to analyze text for specific words or patterns, especially when working with datasets or natural language processing. If you've ever found yourself asking, "How do I compare a list of words with a text to see how many times each word appears?", you're not alone.

In this guide, we’ll explore how to create a dictionary that counts the occurrences of words from a given list within a text using Python. We'll break it down into easy-to-follow sections that will help you understand and implement this functionality effectively.

The Problem

You have two lists:

text: which contains a collection of words.

mylist: which is the dictionary of words you want to track.

For example:

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Your goal is to count how many times each word in mylist appears in text and store these counts in a dictionary.

The Initial Approach

You might start with code similar to this:

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However, this approach will lead to a KeyError when you try to increment a count for a word that hasn’t been initialized in the dictionary.

The Solution

Step-by-Step Implementation

Import the necessary library:
We'll need the collections module for defaultdict.

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Define your lists of words:
Create the lists text and mylist.

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Initialize your dictionary:
Utilize defaultdict(int) to create a dictionary that defaults to 0.

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Count the occurrences:
Iterate through mylist and check if each word is in text. Increment the count in lst_dic.

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Display the results:
Convert lst_dic to a regular dictionary for easier viewing and printing.

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Extract counts greater than 10:
If you want to find words that appear more than 10 times, you can create a list or dictionary comprehension.

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Example Code

Here’s the complete code:

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Conclusion

With this process, you can not only count words more efficiently but also perform further analyses on word frequencies. Happy coding!